• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3197-3218
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• 2012

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user's predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles' physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users' satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

• Geomechanics and Engineering
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• v.21 no.4
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• pp.391-399
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• 2020

Brittleness is one of the most important properties of rock which has a major impact not only on the failure process of intact rock but also on the response of rock mass to tunneling and mining projects. Due to the lack of a universally accepted definition of rock brittleness, a wide range of methods, including direct and indirect methods, have been developed for its measurement. Measuring rock brittleness by direct methods requires special equipment which may lead to financial inconveniences and is usually unavailable in most of rock mechanic laboratories. Accordingly, this study aimed to develop a new strength-based index for predicting rock brittleness based on the obtained base form. To this end, an innovative algorithm was developed in Matlab environment. The utilized algorithm finds the optimal index based on the open access dataset including the results of punch penetration test (PPT), uniaxial compressive and Brazilian tensile strength. Validation of proposed index was checked by the coefficient of determination (R²), the root mean square error (RMSE), and also the variance for account (VAF). The results indicated that among the different brittleness indices, the suggested equation is the most accurate one, since it has the optimal R², RMSE and VAF as 0.912, 3.47 and 89.8%, respectively. It could finally be concluded that, using the proposed brittleness index, rock brittleness can be reliably predicted with a high level of accuracy.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.814-820
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• 2009

With the rapid development of China economy, many engineering projects with large scale and investment were constructed in China and some were the biggest ones in the world. With the development of engineering practice, great progress in the research of engineering management of China was made and a large number of research findings were embodied in content of research papers and were represented by technical words. To know the state of arts in the research field of engineering management in China, three major parts, namely title, abstract and keywords of research papers in last five years from three representative Chinese journals about engineering management were chose as research materials. Unlike western languages, there are no delimiters between the words of Chinese, so the maximum matching and frequency statistics (MMFS) method, a text segmentation technique of text mining Chinese, was presented to extract the features consisting of technical words, phrases and words from the research materials. Recent research and developing trend of engineering management in China were found by comparing and analyzing the difference of technical words in the research materials of last five years.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.627-628
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• 2015

Construction defect which can causes economic damage such as schedule delay, cost overrun is a considerably important factor in construction industry. In general, a construction defect features a difficulty to find out causes precisely because it occurs when several interrelated causes combine. Yet, studies have tried to understand the interrelationships between factors are limited. In addition, despite of a tremendous amount of construction data, it's not still enough to analyze them, but tends to depend on experience or know-how of practitioners. Thus, it is necessary to identify underlying causes in influential factors by utilizing related data. This paper analyses Interrelationships between causal factors using Association Rule Mining to discover root causes of construction defects. Confidence and Lift that can be used for presenting the interrelationships of the causes were extracted from 1241 cases in 30 projects in Korea. It is expected that this paper allows the construction managers to discover key factors and make right decisions to reduce occurrence of construction defects. Furthermore, analysis of interrelationships can improve understanding of structural patterns of construction defects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.spc
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• pp.1-8
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• 2023

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: 'equipment,' 'smart,' and 'eco-friendly.' Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.

• Tunnel and Underground Space
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• v.29 no.1
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• pp.1-11
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• 2019

This study investigated use cases of machine learning technology in domestic medical, manufacturing, finance, automobile, urban sectors and those in overseas mining industry. Through a literature survey, it was found that the machine learning technology has been widely utilized for developing medical image information system, real-time monitoring and fault diagnosis system, security level of information system, autonomous vehicle and integrated city management system. Until now, the use cases have not found in the domestic mining industry, however, several overseas projects have found that introduce the machine learning technology to the mining industry for improving the productivity and safety of mineral exploration or mine development. In the future, the introduction of the machine learning technology to the mining industry is expected to spread gradually.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.85-99
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• 2021

Graphene, called the "dream material" is drawing attention as a groundbreaking new material that will lead the era of the 4th Industrial Revolution. Graphene has high strength, excellent electrical and thermal conductivity, excellent optical permeability, and excellent gas barrier properties. In this paper, as the South Korean government recently announced Green New Deal and Digital New Deal policy, we analyze graphene technology, which is also attracting attention for its application to Corona 19 biosensor, to understand its national R&D trend and knowledge structure, and to explore the possibility of its application. Firstly, 4,054 cases of national R&D project information for the last 10 years are collected from the National Science & Technology Information Service(NTIS) to analyze the trend of graphene-related R&D. Besides, projects classified as green technology are analyzed concerning the government's Green New Deal policy. Secondly, text mining analysis is conducted by collecting 500 recent graphene-related articles from e-newspapers. According to the analysis, the field with the largest number of projects was found to be high-efficiency secondary battery technology, and the proportion of total research funds was also the highest. It is expected that South Korea will lead the development of graphene technology in the future to become a world leader in diverse industries including electric vehicles, cellular phone batteries, next-generation semiconductors, 5G, and biosensors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.841-849
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• 2023

Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

• Proceedings of the Technology Innovation Conference
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• 2003.06a
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• pp.3-11
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• 2003

As the size and scope of R&D investment explodes, the strategic and managerial importance of R&D proposal screening becomes highlighted. This point is particularly true for a large-scale research center that deals with multi-product and multi-technology R&D projects. Despite the importance, however, previous research has focused on project evaluation and selection stage. In this research, we propose a R&D proposal screening system. The main objective of the system is to filter R&D proposals that are identified to be duplications of past or existing projects. To this end, the algorithm of the system employs text mining, multivariate statistical method, and case-based reasoning.

• Geomechanics and Engineering
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• v.15 no.2
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• pp.805-810
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• 2018

The abrasivity of rocks results in tool wear in rock excavation or drilling projects. It can affect significantly the cost and schedule of the projects performed in abrasive rock massess. For this reason, the understanding of the mechanism of rock abrasivity is very important for excavation projects. This study investigates the effect of strength on the LCPC abrasivity coefficient (LAC) for igneous rocks. The LCPT test, the uniaxial compressive strength (UCS) and the Brazilian tensile strength (BTS) tests were carried out on the igneous rock samples. The abrasive mineral content (AMC) was also determined for each rock type. First, the LAC was correlated to the AMC and a very good correlation was found between the two parameters. Then, the multiple regression analysis was carried out by including the AMC, UCS and BTS to the analysis in order to infer the effect of the strength on the LAC. It was seen that the correlation coefficients of multiple regression models were greater than that of the relation between the LAC and the AMC. It is concluded that the AMC is the dominant parameter determining the abrasivity of rock. On the other hand, the rock strength has also significant effect on rock abrasivity.